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Jonas Wallin. Photo.

Jonas Wallin

Senior lecturer, Director of third cycle studies, Department of Statistics

Jonas Wallin. Photo.

Multivariate type G Matérn stochastic partial differential equation random fields

Author

  • David Bolin
  • Jonas Wallin

Summary, in English

For many applications with multivariate data, random-field models capturing departures from Gaussianity within realizations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of stochastic partial differential equations with additive type G noise whose marginal covariance functions are of Matérn type. We consider four increasingly flexible constructions of the noise, where the first two are similar to existing copula-based models. In contrast with these, the last two constructions can model non-Gaussian spatial data without replicates. Computationally efficient methods for likelihood-based parameter estimation and probabilistic prediction are proposed, and the flexibility of the models suggested is illustrated by numerical examples and two statistical applications.

Department/s

  • Department of Statistics

Publishing year

2020-02

Language

English

Pages

215-239

Publication/Series

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volume

82

Issue

1

Document type

Journal article

Publisher

Wiley-Blackwell

Topic

  • Probability Theory and Statistics

Keywords

  • Matérn covariances
  • Multivariate random fields
  • Non-Gaussian models
  • Spatial statistics
  • Stochastic partial differential equations

Status

Published

ISBN/ISSN/Other

  • ISSN: 1369-7412